1. Field of the Invention
In some embodiments, the present invention relates to systems and methods of enhancing resolution of an image. In particular, the present invention relates to suppressing edge ringing artifacts in images.
2. Description of the Related Art
In optics, the modulation transfer function (MTF) characterizes the ability of an optical device to transfer contrast of an image. In a variety of applications, images are collected by image sensors where the specific optics and electronics of image sensors affect the quality of the image. Specifically, high-contrast edges within the actual image are frequently degraded by the sensor. Attenuated edges may result from the reconstruction of a transformed image or the image may simply be defocused.
The images output by an image sensor can be processed by correction algorithms, such as a modulation transfer function correction algorithm or a modulation transfer function compensation algorithm (MTFCs). These techniques amplify the higher spatial frequencies of the image, thereby sharpening edges of an object depicted in the image. Several examples of MTFCs include various Wiener filters, Generalized Inverse Filters (GIFs), Poisson maximum a posteriori non-linear processing, and regularized inverse filters.
Although MTFCs can be used to sharpen images, application of the MTFC frequently results in large edge ringing effects, thereby also degrading image quality. For example, text includes many sharp edges and is a prime candidate for ringing artifacts. Edge ringing produced by MTFC can be particularly pronounced for sparse apertures, multispectral collection systems, and aberrated optical trains. Images collected with sparse apertures are particularly susceptible to aberrations due to tip, tilt, and piston errors, thereby causing ringing effects. Ringing noise in video is visible as local flickering near edges.
Digital images are commonly post-processed to mitigate the effects of artifacts in the reconstructed image. Some post-processing methods attempt to recover the original image from a combination of the decompressed image data and information related to the smoothness properties of the image before compression. In general, post-processing methods are complex, often iterative and time consuming, computationally expensive, and can degrade the sharpness of the image, thereby limiting the usefulness of the methods.